Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Gradient Descent Exer

The video tutorial explains the concept of gradient descent, focusing on why steps are taken in the negative gradient direction. It discusses the role of the gradient vector, the parameter space, and the importance of choosing the right direction to minimize the loss function. The tutorial emphasizes understanding the core question of why the negative gradient direction is preferred, which is crucial for applying gradient descent effectively.

What does the negative sign in the gradient descent equation signify?

• It indicates the direction of the gradient.

• It represents the magnitude of the step.

• It denotes the speed of convergence.

• It signifies moving in the opposite direction of the gradient.

Why is it important to consider the negative gradient direction in parameter space?

• It avoids any changes in the loss function.

• It helps in minimizing the loss function.

• It ensures the fastest increase in the loss function.

• It is the only direction available.

What happens if you move in the direction of the gradient instead of the negative gradient?

• The loss function will increase.

• The loss function will decrease.

• The parameters will remain unchanged.

• The algorithm will converge faster.

What is the primary goal of gradient descent?

• To maximize the loss function.

• To find the maximum value of parameters.

• To minimize the loss function.

• To increase the parameter values.

In the context of gradient descent, what does 'descent' refer to?

• Moving upwards in the parameter space.

• Moving downwards towards the gradient.

• Staying stationary in the parameter space.

• Increasing the parameter values.